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Add more flexible options for model downloads (Proxies, resume_download, local_files_only...) (#1256)
* allow passing more options for model/tokenizer download from remote * temporarily change dependency to current farm master * Add latest docstring and tutorial changes * add kwargs * add docstrings * Add latest docstring and tutorial changes Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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Malte Pietsch
GitHub
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@ -48,7 +48,7 @@ While the underlying model can vary (BERT, Roberta, DistilBERT, ...), the interf
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#### \_\_init\_\_
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```python
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| __init__(model_name_or_path: str, model_version: Optional[str] = None, context_window_size: int = 150, batch_size: int = 50, use_gpu: bool = True, no_ans_boost: float = 0.0, return_no_answer: bool = False, top_k: int = 10, top_k_per_candidate: int = 3, top_k_per_sample: int = 1, num_processes: Optional[int] = None, max_seq_len: int = 256, doc_stride: int = 128, progress_bar: bool = True, duplicate_filtering: int = 0, use_confidence_scores: bool = True)
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| __init__(model_name_or_path: str, model_version: Optional[str] = None, context_window_size: int = 150, batch_size: int = 50, use_gpu: bool = True, no_ans_boost: float = 0.0, return_no_answer: bool = False, top_k: int = 10, top_k_per_candidate: int = 3, top_k_per_sample: int = 1, num_processes: Optional[int] = None, max_seq_len: int = 256, doc_stride: int = 128, progress_bar: bool = True, duplicate_filtering: int = 0, use_confidence_scores: bool = True, proxies=None, local_files_only=False, force_download=False, **kwargs)
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```
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**Arguments**:
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@ -89,6 +89,15 @@ and that FARM includes no_answer in the sorted list of predictions.
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Can be helpful to disable in production deployments to keep the logs clean.
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- `duplicate_filtering`: Answers are filtered based on their position. Both start and end position of the answers are considered.
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The higher the value, answers that are more apart are filtered out. 0 corresponds to exact duplicates. -1 turns off duplicate removal.
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- `use_confidence_scores`: Sets the type of score that is returned with every predicted answer.
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`True` => a scaled confidence / relevance score between [0, 1].
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This score can also be further calibrated on your dataset via self.eval()
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(see https://haystack.deepset.ai/components/reader#confidence-scores) .
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`False` => an unscaled, raw score [-inf, +inf] which is the sum of start and end logit
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from the model for the predicted span.
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- `proxies`: Dict of proxy servers to use for downloading external models. Example: {'http': 'some.proxy:1234', 'http://hostname': 'my.proxy:3111'}
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- `local_files_only`: Whether to force checking for local files only (and forbid downloads)
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- `force_download`: Whether fo force a (re-)download even if the model exists locally in the cache.
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<a name="farm.FARMReader.train"></a>
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#### train
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@ -193,14 +202,14 @@ Example:
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```python
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|{
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| 'query': 'Who is the father of Arya Stark?',
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| 'answers':[
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| {'answer': 'Eddard,',
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| 'context': " She travels with her father, Eddard, to King's Landing when he is ",
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| 'offset_answer_start': 147,
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| 'offset_answer_end': 154,
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| 'answers':[Answer(
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| 'answer': 'Eddard,',
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| 'context': "She travels with her father, Eddard, to King's Landing when he is",
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| 'score': 0.9787139466668613,
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| 'document_id': '1337'
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| },...
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| 'offsets_in_context': [Span(start=29, end=35],
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| 'offsets_in_context': [Span(start=347, end=353],
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| 'document_id': '88d1ed769d003939d3a0d28034464ab2'
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| ),...
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| ]
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|}
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```
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@ -463,7 +463,7 @@ class AdaptiveModel(nn.Module, BaseAdaptiveModel):
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@classmethod
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def convert_from_transformers(cls, model_name_or_path: Union[str, Path], device: str, revision: Optional[str] = None,
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task_type: Optional[str] = None, processor: Optional[Processor] = None):
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task_type: Optional[str] = None, processor: Optional[Processor] = None, **kwargs):
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"""
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Load a (downstream) model from huggingface's transformers format. Use cases:
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- continue training in Haystack (e.g. take a squad QA model and fine-tune on your own data)
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@ -489,7 +489,8 @@ class AdaptiveModel(nn.Module, BaseAdaptiveModel):
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revision=revision,
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device=device,
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task_type=task_type,
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processor=processor)
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processor=processor,
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**kwargs)
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@classmethod
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@ -52,7 +52,11 @@ class FARMReader(BaseReader):
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doc_stride: int = 128,
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progress_bar: bool = True,
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duplicate_filtering: int = 0,
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use_confidence_scores: bool = True
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use_confidence_scores: bool = True,
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proxies=None,
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local_files_only=False,
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force_download=False,
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**kwargs
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):
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"""
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@ -92,6 +96,15 @@ class FARMReader(BaseReader):
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Can be helpful to disable in production deployments to keep the logs clean.
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:param duplicate_filtering: Answers are filtered based on their position. Both start and end position of the answers are considered.
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The higher the value, answers that are more apart are filtered out. 0 corresponds to exact duplicates. -1 turns off duplicate removal.
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:param use_confidence_scores: Sets the type of score that is returned with every predicted answer.
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`True` => a scaled confidence / relevance score between [0, 1].
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This score can also be further calibrated on your dataset via self.eval()
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(see https://haystack.deepset.ai/components/reader#confidence-scores) .
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`False` => an unscaled, raw score [-inf, +inf] which is the sum of start and end logit
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from the model for the predicted span.
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:param proxies: Dict of proxy servers to use for downloading external models. Example: {'http': 'some.proxy:1234', 'http://hostname': 'my.proxy:3111'}
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:param local_files_only: Whether to force checking for local files only (and forbid downloads)
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:param force_download: Whether fo force a (re-)download even if the model exists locally in the cache.
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"""
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# save init parameters to enable export of component config as YAML
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@ -100,7 +113,8 @@ class FARMReader(BaseReader):
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batch_size=batch_size, use_gpu=use_gpu, no_ans_boost=no_ans_boost, return_no_answer=return_no_answer,
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top_k=top_k, top_k_per_candidate=top_k_per_candidate, top_k_per_sample=top_k_per_sample,
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num_processes=num_processes, max_seq_len=max_seq_len, doc_stride=doc_stride, progress_bar=progress_bar,
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duplicate_filtering=duplicate_filtering, use_confidence_scores=use_confidence_scores
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duplicate_filtering=duplicate_filtering, proxies=proxies, local_files_only=local_files_only,
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force_download=force_download, use_confidence_scores=use_confidence_scores, **kwargs
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)
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self.return_no_answers = return_no_answer
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@ -110,7 +124,11 @@ class FARMReader(BaseReader):
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task_type="question_answering", max_seq_len=max_seq_len,
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doc_stride=doc_stride, num_processes=num_processes, revision=model_version,
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disable_tqdm=not progress_bar,
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strict=False)
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strict=False,
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proxies=proxies,
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local_files_only=local_files_only,
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force_download=force_download,
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**kwargs)
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self.inferencer.model.prediction_heads[0].context_window_size = context_window_size
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self.inferencer.model.prediction_heads[0].no_ans_boost = no_ans_boost
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self.inferencer.model.prediction_heads[0].n_best = top_k_per_candidate + 1 # including possible no_answer
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@ -346,7 +364,6 @@ class FARMReader(BaseReader):
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return result
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def predict(self, query: str, documents: List[Document], top_k: Optional[int] = None):
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#TODO update example in docstring
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"""
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Use loaded QA model to find answers for a query in the supplied list of Document.
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@ -355,14 +372,14 @@ class FARMReader(BaseReader):
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```python
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|{
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| 'query': 'Who is the father of Arya Stark?',
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| 'answers':[
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| {'answer': 'Eddard,',
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| 'context': " She travels with her father, Eddard, to King's Landing when he is ",
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| 'offset_answer_start': 147,
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| 'offset_answer_end': 154,
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| 'answers':[Answer(
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| 'answer': 'Eddard,',
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| 'context': "She travels with her father, Eddard, to King's Landing when he is",
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| 'score': 0.9787139466668613,
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| 'document_id': '1337'
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| },...
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| 'offsets_in_context': [Span(start=29, end=35],
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| 'offsets_in_context': [Span(start=347, end=353],
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| 'document_id': '88d1ed769d003939d3a0d28034464ab2'
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| ),...
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| ]
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|}
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```
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@ -52,6 +52,11 @@ def test_prediction_attributes(prediction):
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for ag in attributes_gold:
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assert ag in prediction
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@pytest.mark.slow
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def test_model_download_options():
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# download disabled and model is not cached locally
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with pytest.raises(OSError):
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impossible_reader = FARMReader("mfeb/albert-xxlarge-v2-squad2", local_files_only=True)
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def test_answer_attributes(prediction):
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# TODO Transformers answer also has meta key
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